Download Course Title : Data Mining and Data Warehousing Course Code

Course Title :
Course Code :
Semester:
Credit :
Class Load :
Level :
Data Mining and Data Warehousing
735.3
Fourth
3
3 hours
Master
Sessional
Final
Total
Theory
50
50
100
Practical
-
Total
50
50
100
Course Objective:
The objective of the course is to make understand the data mining and data warehousing principles and
then provide the various techniques for knowledge discovery in large corporate databases.
Course Contents:
1.
Introduction
(4 hrs)
Introduction to data mining, Classification of data-mining systems, Data-mining major issues and
challenges, KDD and DBMS vs Data-mining , Data-mining techniques, Data-mining applications.
2.
Data-warehousing
(5 hrs)
Data-warehousing, Multi-dimensional data model, data-warehousing architecture, data-warehousing
implementation, Data cubes
3.
Data Processing & Data Mining
(12 hrs)
Data Cleaning, Integration, Transformation and Reduction, Discretization and Concept Hierarchy
generation, Data-mining primitives, Knowledge to be mined, data-mining query language, Mining class
comparision, Association Rules, Discovering Association Rule, single Dimensional Boolean Association
Rule, Multilevel Association Rule, Multidimensional Association rule, Algorithms for association rules.
4.
Classification and Prediction
(12 hrs)
Decision trees, Tree construction principle, Tree construction Algorithm, Tree construction with
presorting, Pruning techniques, Integration of pruning and construction. Bayesian Belief network,
Neural Net, Learning in Neural Net. Unsupervised learning, Data mining using neural net, Genetic
algorithm, Rough sets, Support vector machines, Case-based, Fuzzy set; Prediction based on linear and
nonlinear regression, Classifier accuracy.
5.
Cluster Analysis
(6 hrs)
Types of data in cluster analysis, Major clustering methods, partitioning methods, Hierarchical methods,
Density based methods, Grid based methods, Model based clustering methods.
Mining Complex Data Types
(6 hrs)
Mining spatial databases, Multimedia database, Time-series and Sequence data, Web mining, and Text
mining.
References:
1.
Han Jiawei, M. Kamber, "Data Mining Concepts and Techniques" Academic Press, Harcourt India
Private Limited, 2001
2.
Pujari A. K., "Data Mining Techniques" University Press (India) Limited, Hyderabad, India, 2001.
3.
Adriaans Pieter, D. Zantige, " Data Mining", Pearson Education Asia Pte. Ltd, 2002